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Lookup NU author(s): Ammar Karkar, Professor Alex Yakovlev
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Network-on-Chip (NoC) design is attracting more and more attention nowadays, but there is a lack of design optimization method due to the computationally very expensive simulations of NoC. To address this problem, an algorithm, called NoC design optimization based on Gaussian process model assisted differential evolution (NDPAD), is presented. Using the surrogate model-aware evolutionary search (SMAS) framework with the tournament selection based constraint handling method, NDPAD can obtain satisfactory solutions using a limited number of expensive simulations. The evolutionary search strategies and training data selection methods are then investigated to handle integer design parameters in NoC design optimization problems. Comparison shows that comparable or even better design solutions can be obtained compared to standard EAs, and much less computation effort is needed.
Author(s): Wu MY, Karkar A, Liu B, Yakovlev A, Gielen G, Grout V
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2014 IEEE Congress on Evolutionary Computation
Year of Conference: 2014
Pages: 3266-3271
Print publication date: 01/01/2014
Online publication date: 22/09/2014
Acceptance date: 06/07/2014
Publisher: IEEE
URL: https://doi.org/10.1109/CEC.2014.6900559
DOI: 10.1109/CEC.2014.6900559
